Tracing the Footprints of AI in Radiology Literature: A Detailed Analysis of Journal Abstracts

Abstract

Purpose To assess and compare the probabilities of AI-generated content within scientific abstracts from selected Q1 journals in the fields of radiology, nuclear medicine, and imaging, published between May and August 2022 and May and August 2023. Materials and Methods An extensive list of Q1 journals was acquired from Scopus in the fields of radiology, nuclear medicine, and imaging. All articles in these journals were acquired from the Medline databases, focusing on articles published between May and August in 2022 and 2023. The study specifically compared abstracts for limitations of the AI detection tool in terms of word constraints. Extracted abstracts from the two different periods were categorized into two groups, and each abstract was analyzed using the AI detection tool, a system capable of distinguishing between human and AI-generated content with a validated accuracy of 97.06 %. This tool assessed the probability of each abstract being AI-generated, enabling an in-depth comparison between the two groups in terms of the prevalence of AI-generated content probability. Results Group 1 and Group 2 exhibit significant variations in the characteristics of AI-generated content probability. Group 1, consisting of 4,727 abstracts, has a median AI-generated content probability of 3.8 % (IQR1.9–9.9 %) and peaks at 49.9 %, with the computation times contained within a range of 2 to 10 seconds (IQR 3–8 s). In contrast, Group 2, which is composed of 3,917 abstracts, displays a significantly higher median AI-generated content probability at 5.7 % (IQR2.8–12.9 %) surging to a maximum of 69.9 %, with computation times spanning from 2 to 14 seconds (IQR 4–11 s). This comparison yields a statistically significant difference in median AI-generated content probability between the two groups (p = 0.005). No significant correlation was observed between word count and AI probability, as well as between article type, primarily original articles and reviews, and AI probability, indicating that AI probability is independent of these factors. Conclusion The comprehensive analysis reveals significant differences and variations in AI-generated content probabilities between 2022 and 2023, indicating a growing presence of AI-generated content. However, it also illustrates that abstract length or article type does not impact the likelihood of content being AI-generated. Key Points: